Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Nominal rigidity wikipedia , lookup
Exchange rate wikipedia , lookup
Edmund Phelps wikipedia , lookup
Monetary policy wikipedia , lookup
Business cycle wikipedia , lookup
Fear of floating wikipedia , lookup
Interest rate wikipedia , lookup
Okishio's theorem wikipedia , lookup
Transformation in economics wikipedia , lookup
- 1 - How high is the natural rate of unemployment in Hong Kong? (A technical note) Introduction Natural rate of unemployment, which refers to a situation of full employment and stable inflation, is one of the core issues in economic analysis with significant implications in various policy arenas. This paper provides a technical review of the estimation of the time-varying natural rate of unemployment, as measured by the non-accelerating inflation rate of unemployment (NAIRU), over the past two decades (1994 - 2013), organized as follows. Part 1 describes the methodology of the NAIRU estimation. Part 2 reports the model estimation results. Detailed discussion on the estimated NAIRU series and its salient features are given in Part 3. Part 1. Methodology 2. The empirical estimation of NAIRU broadly follows the analytical framework as set out by Adams and Coe (1990) for estimation of the natural rate of unemployment. A system of simultaneous equations is developed to explain aggregate wage and price setting behavior, whilst also taking into account the supply side factors as well as the structural aspects in Hong Kong’s labour market. The use of simultaneous equation system has the merit that the natural rate of unemployment and potential output can be jointly determined, while integrating the wage and price relationship with the relevant cyclical and structural factors. The resultant estimates of NAIRU and potential output, as well as the output gap so derived, are thus fully consistent with each other. 3. In this exercise, we distinguish between general inflation and consumer price inflation in inflation modelling, as recent data suggest that wage is more likely to be determined by the consumer price inflation whilst the unit labour cost tends to be determined by wage cost relative to general inflation. Meanwhile, we have also taken into account the structural change to the wage dynamics from the implementation of the Statutory Minimum Wage (SMW) in 2011 and its subsequent uprating in 2013. In specific, the structural equations are set out in details, as follows. - 2 - (1) Consumer price inflation equation – UCCPI ∆ CPI = f (∆ w , Y − 1, Yp ∆( Y ), N ∆ ( MP ), Vac ) 4. The inflation series, ∆CPI , used in the model is the rate of change in the underlying Composite Consumer Price Index (UCCPI), which excludes the effects of all Government’s one-off relief measures. The movements of wage growth, ∆w , net of labour productivity growth, ∆(Y / N ) , features prominently as the key determinant of locally generated cost pressures, whilst the overall vacancy rate, Vac ( 1 ), is to capture the impact on consumer price inflation due to rental movements from the property market cycles. Import prices of food and consumer goods, ∆(MP) , are to gauge the price pressures from external sources (Chart 1). The extent of these domestic and external influences that can be passed through to the local retail price level would be reflected in a change in profit margin as proxied by the change in output gap, Y / YP − 1 , a key measures of the aggregate demand and supply balance in the local economy(2). Chart 1 Consumer price inflation and its underlying local and external factors (a) Domestic 15 (b) External Percent 15 Wage inflation 10 10 5 5 0 0 -5 Percent Consumer price inflation* Import UVI of food and consumer goods# -5 Consumer price inflation* -10 -10 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 Notes : (*) In terms of Underlying Composite CPI. (#) Weighted average. (1) Overall vacancy rate refers to the average vacancy rate of the private residential flats, office, commercial and retail spaces, and flatted factories, weighted by their corresponding value at 2000 prices. (2) The potential output YP featured in the output gap is derived from a separately estimated production function, assuming full employment in the labour market, and with the stock of machinery and building investment utilised at their normal intensity of usage. Please see the paragraphs 9-11 for further elaboration. - 3 - (2) General inflation equation – GDP Deflator = f ( ∆ CPI , ∆ GDPD Y − 1, Yp ∆( Px )) Pm 5. General inflation, ∆GDPD , as measured by the rate of change in the GDP deflator, is the broadest measure of inflation in the economy. Alongside the consumer price inflation and output gap as explanatory variables, the change in terms of trade, ∆( Px / Pm ) , is included to capture the terms of trade shock from the external environment (e.g. oil price hike; exchange rate fluctuations) (Chart 2). Chart 2 (a) General inflation and CPI inflation 15 10 Percent (b) General inflation and External factors 15 Consumer price inflation* Percent 10 GDP deflator 5 5 0 0 -5 Term of trade# as measure of external supply shock -5 GDP deflator -10 -10 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 Notes : (*) In terms of Underlying Composite CPI. (#) 4-quarter moving average. (3) Phillip’s Curve ∆w = g (∆CPI , u − un , ∆( Y ), N , D _ SMW11 , ∆SMW13 ) 6. This is a typical Phillips curve where in the long run, the wage growth trend is linked to consumer price inflation, ∆CPI , and labour productivity growth, ∆(Y / N ) . But in the short run, nominal wage growth will adjust in response to the dis-equilibrium in the labour market, as depicted by the deviation of the unemployment rate u from its natural rate un, the latter being a measure of the degree of tightness in the labour market (Chart 3). Meanwhile, by incorporating directly the one-off impact on nominal wages from the implementation of the SMW in 2011, D _ SMW11 , and the rate of increase in SMW rate in 2013, ∆SMW13 , which are separately calibrated, the distortionary impact from SMW upon the Phillip’s Curve has been netted out. - 4 Chart 3 Phillips curve for Hong Kong Wage inflation (%) 14 90 89 12 1985 - 1990 91 10 88 93 94 1991 - 1997 92 1998 - 2003 87 8 11 (Implementation of SMW) 2004 - 2013 95 97 6 96 85 12 86 13 4 98 08 07 10 06 2 01 00 0 05 02 99 09 -2 04 03 -4 0 1 2 3 4 5 6 7 8 9 Unemployment rate (%) (4) Unemployment rate equation u = h( ∆ ( w⋅ N Y ), − 1, σ , mpf , cssa, Y ⋅ GDPD Yp c − c, I c − I c , demo _ factor ) 7. This is an augmented Okun’s Law equation, which relates the level of unemployment to the balance of demand and supply in the macro economy as depicted by the output gap. The unit labour cost, as measured by ∆(w ⋅ N / Y ⋅ GDPD) , features as a key factor affecting the level of unemployment rate, in the sense that if unit labour cost can adjust downwards flexibly after a major macro-economic shock, the burden of adjustment will fall less on unemployment (Chart 4). The inclusion of the short-run fluctuation of the GDP shares of consumption and construction, denoted by c − c and Ι c − Ι c respectively, is to capture the cyclicality factor caused by unbalanced growth pattern. This is to take on board the earlier analysis that consumption is the most effective driver in creating employment among all the components in the GDP, and also factoring in the immense tightness in construction sector despite sub-par economic growth in recent years. - 5 - 8. As to the structural factors, the dispersion of unemployment rate across occupation (σ ) ( 3 ) is to capture the skill mismatch in the process of economic restructuring. Also, following Adams and Coe (1990), the impact on unemployment rate due to the changes in the age-gender-education composition, as denoted by demo _ factor , which has been separately estimated using constant labour shares, enters directly into the unemployment equation(4). Finally, two variables have been added to filter the effects on the unemployment rates from the ratio of CSSA payment for the unemployment to wage index, cssa , and the employers’ contribution in Mandatory Provident Fund, mpf , this being proxied by the share of payment in kind of the compensation of employee. Chart 4 (a) High unemployment in late 90s being affected by rising unit labour cost 56 Percent Percent 9 0.50 (b) Dispersion of unemployment declining in recent years Percent Percent Standard deviation of unemployment rate across occupations (left scale) 8 54 Unit labour cost* (left scale) 52 7 0.45 6 5 50 Unemployment rate (right scale) Unemployment rate (right scale) 7 6 4 3 3 2 8 5 0.40 4 48 9 0.35 2 46 1 1 44 0 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 0.30 0 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Note: (*) Compensation of employees as a ratio of GDP. (3) σ is defined by σ 2 = ∑ wi ui − 1 u 2 , where wi is the labour force share of the i th occupation. (4) It essentially means using the age-gender-education adjusted unemployment rate series in the estimation of parameters for the various cyclical and structural factors in the unemployment rate equation, i.e. having already filtered the effect of demographic changes on unemployment rate. For details of the series, see Annexes I and II. - 6 - The production function 9. Potential output is taken as an economy’s production capacity under normal intensity of usage of its factors of production. In our existing framework, a Cobb-Douglas production function is adopted to provide a functional form for modeling output in terms of the underlying factor inputs, i.e. labour and capital stock. For the purpose of estimating the production function, data series on two types of capital stock, viz. building stock and machinery stock, have been compiled by aggregating the corresponding gross investment flows, net of depreciation which is assumed to follow a declining-balance schedule with varying depreciation rates on different investment components(5). 10. Under this specification, the actual level of output can be expressed as a function of the actual employment, occupied building stock and machinery stock, adjusted for variations in the intensity of usage of factor inputs. That is: log(Yt ) = a1 log(lt Lt ) + a2 log(bt Bt ) + a3 log(mt M t ) + a4 log( SERt ) = a1 log( Lt ) + a2 log( Bt ) + a3 log(M t ) + {a1 log(lt ) + a2 log(bt ) + a3 log(mt )} + a4 log( SERt ) where: L B M l,b,m SER = = = = = Total employment Occupied building stock Machinery stock Intensity of usage of various factor inputs(6) Share of employment in services sector(7) (5) The assumed depreciation rates for machinery stock and building stock, though necessarily with some degree of subjectivity, are the result of a sensitivity search after testing different depreciation rate assumptions to derive the potential GDP and output gap estimates that can better explain the price trend in Hong Kong. For details, see the “Growth accounting framework applied to Hong Kong – A technical note”. (6) The inclusion of measures of intensity of usage in the production function is based on the proposition that any unexpected variations in output in the short run are usually absorbed by changes in the intensity of usage of the factor inputs, as adjustment in the levels of capital and labour will take some time to achieve. Instead of separately quantifying the intensity of usage for each type of factor inputs, the overall intensity of usage of factor inputs is proxied by: {a1 log(lt)+a2 log(bt)+a3 log(mt)} = {a5 log(It)+a6 log(It)L} where It is defined as the ratio of the growth rate of Yt to its 11-year centred moving average, and (It)L is a geometrically distributed lag variable beginning in period t-1. (7) The inclusion of share of employment in services sector in the production function is to capture the additional productivity gain arising from the economic re-structuring towards services sector over the past three decades or so. - 7 Chart 5 Potential GDP as obtained from the estimated production function 220 Index (1994 = 100) 200 Potential GDP 180 160 Capital stock 140 120 Labour force 100 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 11. The potential output is obtained from the estimated parameters( 8 ), assuming full-employment condition and also with normal intensity of usage of factor inputs, i.e. when the unemployment rate is at its natural rate and It=1(9) (Chart 5). Part 2: Model estimation results 12. The structural equations are re-calibrated with the entire estimation period from Q1 1994 to Q4 2013. Because the natural rate of unemployment is both an input variable to the production function for estimating the potential output and a (8) The production function is estimated using annual data from 1986 to 2013. The estimated parameters are all correctly signed (corresponding t-statistics depicted in brackets) : Log ( Yt ) log( Lt ) log( Bt ) log( M t ) log( SERt ) Log( I t ) log ( I t ) L 0.5000 0.1971 0.2854 0.2525 0.8049 3.5584 (2.95) (3.87) (1.41) (8.19) (5.23) 2 R =0.9977 * The t-statistics are in the parenthesis. The autoregressiveness of shocks have been taken into account during the estimation. Note that the coefficient of log( Lt ) is set to be 0.5, which is the long-run average of the share of compensation of employers in the GDP as obtained in the national income accounts. (9) Another common method of estimating potential output, viz. the Hodrick-Prescott (HP) Filter, has also been tried out. Yet the resultant estimate of output gap is less correlated with the core inflation rate, suggesting that it is not as good an indicator of the overall supply-demand balance as that obtained from the Cobb-Douglas production function. Also, the choice of sampling period when using the HP Filter has a relatively significant bearing on the ultimate output gap estimate, due to the inherent limitation of this smoothing technique. - 8 - variable to be ascertained from the simultaneous equation model, an iterative procedure is carried out until the resultant estimates of the natural rate of unemployment and the output gap so derived converge in successive runs. The parameters and the key diagnostic statistics of the model are in the table below. The charts on simulated vs actual values of the four equations indicate that the model is able to capture the turning points well (Chart 6(a)-(d)). Equation (1) (2) (3) (4) Variables ∆w ∆CPI ∆GDPD ∆w u φ L (∆w) 0.4818 0.2056* (+6.15) (+6.58) -0.2056* (+6.15) ∆GDPD 0.9513 (24.29) ∆CPI 0.5640 (+12.18) -0.5059 (-4.38) u − un Y Yp 0.3145 (6.71) -0.1612 (-3.18) − 1 ∆( Y ) N ∆( Px ) Pm 0.0058 (+4.17) -0.1999 (-8.16) -0.2056* (+6.15) 0.6131 (+10.52) 0.2492 (+6.19) 0.1142 ∆(MP) (3.01) -0.0072 VACt (-3.69) -0.2219 c−c (-8.03) -0.0006 Ic − Ic (-4.02) 0.0225 (+1.96) σ mpf 0.6946 (+4.41) cssa 0.0062 (2.31) Dummy for 2003 0.0103 (+3.00) Asian Financial Crisis dummy -0.0224 (-6.72) -0.023 (-3.78) Dummy for 2013 Adjusted R-Square D-W Statistics 0.9190 0.4316 0.8664 0.9887 0.8697 0.4741 0.9091 0.8203 Theil 0.1729 0.2809 0.2383 0.1023 t-statistics are in the parenthesis; Chow tests indicate that there have been no structural breaks throughout the estimation period. * ∆ ∆ W − ∆ P − ∆ ( Y ) N For the unemployment equation, unit labour cost growth is represented by , hence restrictions have been placed on the coefficients for these three variables in the estimation process accordingly. denotes rate of change - 9 - - 10 - - 11 - 13. By definition, the non-accelerating inflation rate of unemployment, u n , corresponds to the situation when wage growth and inflation are both steady (i.e. non-accelerating), while GDP is at its full potential level, net of all cyclical factors; the labour market is in full employment and the demand/supply situation of the property market (as proxied by the vacancy rate) is at its long-term average. Technically, this implies the following steady state conditions: 1. 2. 3. 4. 5. 6. 7. 8. ∆GDPDt = ∆GDPDt −1 ∆CPI t = ∆CPI t −1 ∆wt = ∆wt −1 u t = u t −1 = u n Yt = YP N t = N P = Lt (1 − u n ) Vact = Vac , i.e. long-term average wage = steady state wage level under full employment conditions, i.e. clear of cyclical disturbances. Part 3. NAIRU estimates 14. Applying the above steady state conditions to solve for the reduced form, the natural rate of unemployment, u n , can be computed as follows: un = demo _ factor − 0.0942∆( Y ) + 0.0225σ + 0.0045 D _ SMW11 + 0.0061∆SMW13 − 0.0396 N 15. As the reduced form equation shows, a faster increase in labour productivity growth will mean a lower natural rate of unemployment. This highlights the importance of investment in education to raise the quality of the workforce to fit in with the needs of a knowledge-based economy. Enhanced labour productivity is indeed the key to sustainable economic growth, particularly in face of an ageing population. 16. Another important determinant in the NAIRU’s reduced form is the dispersion of unemployment rate, usually taken as a proxy of the extent of skill mismatch. The positive sign suggests that NAIRU will rise with a higher dispersion of unemployment when the economy shifts to high value-added activities without adequate investment in education and job upgrading. That the proxy for the extent of skill mismatch narrows in recent years reflects the past effort of sizable investment in education and job upgrading of the workforce, thus providing some cushion against the upward pressure on NAIRU in recent years (Chart 4b). Once again, the policy implication is very clear – the importance to tackle skill mismatch through life-long learning, job training and retraining. - 12 - 17. Added to the reduced form equation are the dummy for implementation of the SMW in 2011, D _ SMW11 , and the rate of change in SWM rate, ∆SMW13 , in 2013. The positive signs of these factors suggest that NAIRU has been subject to upward pressures at the onset and subsequent uprating of the SMW in 2011 and 2013 respectively. This underscores the significance to maintain a balance between the SMW’s objectives of preventing unreasonably low wages and minimizing the loss of low-paid jobs, especially if the labour productivity growth provide little cushion against the upward pressure on NAIRU amidst less sanguine economic conditions. 18. In short, the impact of economic structural change on the long-run unemployment rate actually comprises three forces at play (1) the rise of service industries lifts up labour productivity over time, and in turn lowers the natural rate of unemployment through productivity gains and job generation in a broad spectrum of activities (the productivity factor); (2) there could be a growing number of workers who find their skills obsolete and are struggling to be employed if no appropriate training and retraining (the skill mismatch factor); (3) the SMW implementation and uprating could raise the labour cost of the business, and thus push up the natural rate of unemployment. That the NAIRU estimates for Hong Kong remained broadly stable over the past 20 years indicates that at least empirically, the impact of economic restructuring on structural unemployment has not been as significant as many people have thought – thanks again to the hefty investment in education and continuous job upgrading of the existing workforce. - 13 - Table 1: Estimated results of the natural rate of unemployment Natural rate of unemployment (%) Actual unemployment rate (%) Labour market gap* (%) 1994-98 (5 years) 3.7 3.0 -0.7 1999-2003 (5 years) 3.7 6.3 2.6 2004-08 (5 years) 4.0 4.9 0.9 2009-13 (5 years) 3.9 3.9 0.0 Annual average for: Note: (*) Difference between actual unemployment rate and the natural rate of unemployment; positive figure indicates excess labour supply relative to labour demand, negative figure indicates excess labour demand relative to labour supply. - 14 - 19. Estimates of the natural rate of unemployment over the period 19942013 are shown in Table 1, along with the actual unemployment rate. A number of salient observations can be made by analyzing the NAIRU estimates in the context of the macro developments of the Hong Kong economy in the 1990s and recent years: (1) Over the entire time span of estimation, NAIRU held largely stable, with the long-term average of 3.8% (Chart 7). Chart 7 NAIRU held largely steady over the past two decades Percent 10 Unemployment rate NAIRU (4-quarter moving average) 8 Labour market gap* 6 4 2 0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 -2 -4 -6 Note: (*) Difference between actual unemployment rate and NAIRU. Inverted scale. (2) From the mid-1990s and up until 1997 when the economy was buoyant, notwithstanding a temporary relapse in 1995, the actual unemployment rate was persistently below the natural rate of unemployment by a considerable margin. The tight labour market condition thus ensued manifested itself also in a persistently positive output gap indicating supply bottlenecks and an overheated economy. This in turn generated significant upward pressures on nominal wages and hence consumer prices, which surged by an annual average of 7.6% and 7.5% respectively between 1994 and 1997 (Chart 8 and Chart 9). (3) The macro picture changed completely in 1998, when Hong Kong was thrown into a serious recession in the aftermath of the Asian financial turmoil and property market bubble burst. With the slackening in aggregate demand, output gap turned negative in 1998, signifying a severe slump which led to the onset of a 68-month long deflation era. In tandem with this, manpower resource balance swung from labour market tightness (i.e. negative unemployment gap) in the pre-1998 period to persistently inadequate demand - 15 - and labour supply glut (i.e. positive unemployment gap) after 1998. The unemployment rate shot up from just above 2% in late 1997 to 6.5% in Q3 1999, causing nominal wage to fall as part of the adjustment process to restore external competitiveness and a better balance in the labour market. Despite a brief improvement in 2000, unemployment rate soared again to a historical high of 8.5% in Q2 2003, resulting in a renewed widening in unemployment gap and declining in nominal wages. Year-on-year rate of change Chart 8 Wages adjusting in response to changes in labour demand/supply balance Percent 6 Labour market gap* (left scale) 14 12 Change in nominal wage rate (right scale) 10 4 8 6 2 4 2 0 0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 -2 -4 -2 -6 -8 -4 -10 -12 -6 -14 Note: (*) Difference between actual unemployment rate and NAIRU. The scale has been inverted to facilitate matching with the nominal wage. (4) As the economy entered into a V-shaped rebound in 2004 after the waning of the negative impacts due to the SARS outbreak in 2003, the aggregate demand/supply balance improved in tandem, and switched back to the positive region in early 2005. Nominal wages resumed increase since 2005 Q1 alongside with gradual pick-up in CPI inflation. From 2004 onwards when the economy sustained a robust growth momentum, unemployment rate dwindled successively from the all-time high in 2003 to slightly over 3% in more recent years, only interrupted by a brief relapse in 2009 amid the Global Financial Crisis. In light of the tightened manpower resource balance, coupled with the SMW implementation and uprating in 2011 and 2013 respectively, nominal wage and consumer price inflation picked up notably between 2010 and 2013. - 16 - (5) Indeed, the macro trends in nominal wages and CPI inflation over the entire period 1994-2013 are highly consistent with the movements of the estimated output gap and labour market gap (Chart 8 and Chart 9), as in the preceding exercises. This can also be confirmed from the strong correlation between nominal wages and labour market gap (0.89); and between the consumer price inflation and the estimated output gap (0.85). Percent Year-on-year rate of change Chart 9 Inflation moves broadly in sync with the aggregate demand/supply balance 10 10 Output gap# (left scale) 8 CPI Inflation * (right scale) 8 6 6 4 4 2 2 0 0 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 -2 -2 -4 -4 -6 -6 -8 -8 -10 -10 Notes: (#) 4-quarter moving average. (*) In terms of underlying Composite CPI (6) The model-run results also suggest that wage flexibility in Hong Kong’s labour market has been hampered somewhat during the deflation period in 1998-2003. Conceivably, nominal wages are easier to adjust upward when inflation is high, but may be more rigid in downward adjustments when prices are falling. Because of this, unit labour cost (payroll cost per $ of output) might have stayed higher than the desired level throughout the deflation era (Chart 4a), and the labour market had to adjust both by way of nominal wage decline, and also through retrenchment and lay-offs amidst the deflationary spiral. This may explain partly why the unemployment rate had stayed high in most of the deflation period despite the rebound in real activity. (7) The high unit labour cost during 1998-2003 in fact signified the need to scale back on labour inputs when business outlook deteriorated with the cyclical downturn at that time. But as the economy subsequently recovered and business returned, unit labour cost has fallen back successively, indicating that much of the slack in labour market has been worked off by around 2007. - 17 - (8) With unemployment rate at 3.4% in 2013, versus a natural rate of unemployment of around 4%, manpower shortage is evident at the macro level, more so in individual sectors. The issue of skill mismatch remains a concern. Beyond the short term cyclicality, there is the challenge from population ageing, with labour force set to dwindle after 2018. This makes it all the more important for the Government to continue to invest in higher education and in job training and retraining, so as to raise productivity for a sustainable economic development over the longer run. July 2014 - 18 - Annex I Population by age group and gender % share in total population Age group 2000 2001 2002 2003 2004 2005 2006 0-14 16.9 16.4 15.9 15.4 14.8 14.2 13.7 15-24 13.9 13.6 13.2 13.1 13.3 13.3 13.3 25-29 8.0 7.8 7.6 7.3 7.2 7.1 7.3 30-39 19.1 18.8 18.5 18.0 17.4 16.8 16.4 40-49 17.6 18.1 18.7 19.2 19.5 19.6 19.4 50-59 9.6 10.3 11.0 11.7 12.3 13.2 13.9 14.8 15.0 15.1 15.3 15.5 15.7 16.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 24.5 25.2 26.1 26.9 27.8 28.9 29.9 Male 49.2 48.9 48.6 48.4 48.2 47.9 47.7 Female 50.8 51.1 51.4 51.6 51.8 52.1 52.3 60 and above Total Of which : 50 and above Gender group - 19 - Population by age group and gender % share in total population Age group 2007 2008 2009 2010 2011 2012 2013 0-14 13.3 12.9 12.4 12.0 11.6 11.4 11.1 15-24 13.1 12.9 12.7 12.5 12.4 12.3 12.0 25-29 7.4 7.6 7.6 7.6 7.6 7.4 7.2 30-39 16.3 16.0 15.9 15.8 15.7 15.8 15.8 40-49 18.9 18.5 18.2 17.7 17.3 17.0 16.5 50-59 14.4 15.0 15.5 15.9 16.3 16.5 17.0 60 and above 16.6 17.0 17.7 18.4 19.1 19.7 20.4 100.0 100.0 100.0 100.0 100.0 100.0 100.0 31.0 32.0 33.2 34.3 35.4 36.2 37.4 Male 47.5 47.3 47.1 46.9 46.7 46.5 46.3 Female 52.5 52.7 52.9 53.1 53.3 53.5 53.7 Total Of which : 50 and above Gender group - 20 - Annex II Actual and demographically adjusted unemployment rates (A) (B) (C) (D) Actual Gender adjusted Age adjusted Education adjusted (E) Gender-ageeducation adjusted (F) Demofactor (F) = (A) – (E) 1997 Q1 Q2 Q3 Q4 2.2 2.1 2.2 2.3 2.2 2.1 2.2 2.3 2.2 2.1 2.2 2.3 2.2 2.1 2.2 2.3 2.2 2.1 2.2 2.3 0.0 0.0 0.0 0.0 1998 Q1 Q2 Q3 Q4 3.3 4.3 5.3 5.9 3.3 4.3 5.3 5.9 3.2 4.3 5.4 5.8 3.3 4.3 5.4 5.9 3.3 4.3 5.4 5.9 0.0 0.0 0.0 -0.1 1999 Q1 Q2 Q3 Q4 6.2 6.1 6.5 6.2 6.2 6.1 6.5 6.3 6.2 6.2 6.5 6.2 6.2 6.1 6.5 6.3 6.3 6.3 6.6 6.4 -0.2 -0.2 -0.1 -0.2 2000 Q1 Q2 Q3 Q4 5.5 5.0 4.9 4.4 5.5 5.0 5.0 4.4 5.5 5.1 5.0 4.4 5.6 5.1 5.0 4.5 5.8 5.2 5.2 4.7 -0.3 -0.2 -0.3 -0.3 2001 Q1 Q2 Q3 Q4 4.4 4.5 5.4 6.1 4.5 4.5 5.5 6.2 4.5 4.6 5.5 6.2 4.5 4.6 5.5 6.2 4.8 4.9 5.8 6.6 -0.4 -0.4 -0.4 -0.6 2002 Q1 Q2 Q3 Q4 6.9 7.6 7.6 7.2 7.0 7.7 7.7 7.3 7.0 7.7 7.7 7.3 7.1 7.8 7.9 7.6 7.6 8.2 8.3 8.1 -0.7 -0.7 -0.7 -0.9 2003 Q1 Q2 Q3 Q4 7.4 8.5 8.5 7.2 7.5 8.7 8.7 7.3 7.4 8.7 8.7 7.2 7.8 8.9 8.9 7.6 8.3 9.7 9.6 8.0 -0.9 -1.2 -1.1 -0.8 2004 Q1 Q2 Q3 Q4 7.1 6.8 7.0 6.3 7.2 6.9 7.1 6.4 7.1 6.8 7.0 6.2 7.6 7.2 7.4 6.8 8.1 7.6 7.8 7.0 -1.0 -0.8 -0.8 -0.7 2005 Q1 Q2 Q3 Q4 5.9 5.7 5.7 5.0 6.0 5.8 5.8 5.1 5.8 5.8 5.7 5.0 6.3 6.2 6.0 5.4 6.8 6.7 6.5 6.1 -0.9 -1.0 -0.8 -1.0 Notes : The adjusted unemployed rate for a given quarter is compiled by standardising the composition of labour force by the demographic factor(s) concerned in that quarter to the corresponding composition in 1997. All unemployment rates have not been adjusted for seasonality. - 21 - Actual and demographically adjusted unemployment rates (A) (B) (C) (D) Actual Gender adjusted Age adjusted Education adjusted (E) Gender-ageeducation adjusted (F) Demofactor (F) = (A) – (E) 2006 Q1 Q2 Q3 Q4 5.0 5.0 4.9 4.2 5.1 5.1 5.0 4.4 5.0 5.2 5.1 4.3 5.4 5.3 5.2 4.6 6.0 6.0 5.6 5.2 -1.0 -1.0 -0.7 -0.9 2007 Q1 Q2 Q3 Q4 4.2 4.3 4.3 3.3 4.3 4.4 4.4 3.3 4.2 4.5 4.5 3.4 4.6 4.7 4.5 3.5 5.0 5.4 5.1 3.9 -0.8 -1.1 -0.8 -0.7 2008 Q1 Q2 Q3 Q4 3.2 3.3 3.7 3.9 3.3 3.4 3.7 3.9 3.3 3.5 3.8 4.1 3.5 3.6 3.9 4.0 3.9 3.9 4.3 4.7 -0.6 -0.6 -0.7 -0.9 2009 Q1 Q2 Q3 Q4 5.1 5.5 5.7 4.7 5.2 5.6 5.8 4.8 5.3 5.8 6.0 5.1 5.5 5.8 5.8 4.9 6.3 6.9 6.7 5.9 -1.2 -1.4 -1.0 -1.1 2010 Q1 Q2 Q3 Q4 4.4 4.8 4.4 3.7 4.6 4.9 4.5 3.8 4.8 5.2 4.9 4.2 4.7 5.0 4.5 3.8 5.7 5.8 5.4 4.7 -1.2 -1.1 -1.0 -1.0 2011 Q1 Q2 Q3 Q4 3.4 3.7 3.4 3.1 3.5 3.8 3.4 3.2 3.8 4.1 3.8 3.3 3.6 3.9 3.5 3.3 4.6 4.9 4.4 4.2 -1.2 -1.2 -1.0 -1.1 2012 Q1 Q2 Q3 Q4 3.3 3.3 3.5 3.1 3.4 3.4 3.5 3.2 3.5 3.8 4.0 3.3 3.5 3.5 3.5 3.2 4.3 4.7 4.2 4.1 -1.0 -1.4 -0.7 -1.0 2013 Q1 Q2 Q3 Q4 3.4 3.4 3.5 3.1 3.5 3.5 3.5 3.1 3.7 3.8 3.9 3.4 3.7 3.6 3.5 3.2 4.5 4.6 4.4 4.3 -1.1 -1.2 -0.9 -1.2 Notes : The adjusted unemployed rate for a given quarter is compiled by standardising the composition of labour force by the demographic factor(s) concerned in that quarter to the corresponding composition in 1997. All unemployment rates have not been adjusted for seasonality. - 22 - References Adams, Charles and David T. Coe. 1990. “A Systems Approach to Estimating the Natural Rate of Unemployment and Potential Output for the United States”, Staff Papers, IMF, Vol. 37, No. 2 Aricó, Fabio. 2003 “Growth and Unemployment: Towards a Theoretical Integration”, in Surveys in Economic Growth: Theory and Empirics. D. A. R. George; L. Oxley and K. I. Carlaw, eds. Blackwell Publishing Ball, Laurence. 1996. “Disinflation and the NAIRU”, NBER Working Paper 5520 Ball, Laurence; N. Gregory Mankiw. 2002. “The NAIRU in Theory and Practice”, NBER Working Paper 8940 Balnchard, Oliver; Lawrence F. Katz. 1996. “What we know and do not know about the Natural Rate of Unemployment”, NBER Working Paper Series Working Paper 8522 Bleaklye, Hoyt; Jeffrey C Fuhrer. 1997. “Shifts-in the Beveridge Curve, Job Matching, and Labour Market Dynamics”, New England Economic Review Sep/Oct 1997 Coe, David T. 1985. “Nominal Wages, the NAIRU and Wage Flexibility”, OECD Economic Studies No.5 Elmeskow, Jørgen. 1993. “High and Persistent Unemployment : Assessment of the Problem and its Causes”, OECD Economic Department Working Papers No. 132 Fabiani, Silvia; Richardo Mestre. 2002. “Alternative Measures of the NAIRU in the Euro Area: Estimates and Assessment”, European Central Bank Working Paper No. 17 Giorno, Claude; Pete Richardson Deborah Roseveare and Paul van den Noord. 1995 ‘Estimating Potential Output, Output Gaps and Structural Budget Balances”, OECD Economic Department Working Paper No. 152 Gordon, Robert J. 1996. “The Time-varying NAIRU and its Implications for Economic Policy”, NBER Working Paper 5735 - 23 Groshen, Erica L.; Simon Potter. 2003. “Has Structural Change Contributed to a Jobless Recovery?”, Current Issues in Economics and Finance Vol. 9, No. 8 Pang, Wensheng; Lillian Cheung and Kelvin Fan. 2001. “Sources of Unemployment: Recent Developments and Prospect”, HKMA Quarterly Bulletin 11/2001 Santomero, Anthony M.; John J. Seater. 1978. “The Inflation-Unemployment TradeOffs: A Critique of the Literature”, Journal of Economic Literature, Vol. 16, No. 2 Song, Lei Lei; Elizabeth Webster. 2003. “How Segmented are Skilled and Unskilled Labour Markets : The Case of Beveridge Curves”, Australian Economic Papers, Volume 42, Number 3 Staiger, Douglas; James H Stock; Mark W Watson. 2001. “Prices, Wages and the US NAIRU in the 1990s”, NBER Working Paper 8320 Turner, Dave; Laurence Boone; Claude Giorno; Mara Meacci; Dave Rae and Pete Richardson. 2001. “Estimating the Structural Rate of Unemployment for the OECD Countries”, OECD Economic Studies No.33 Torres, Raymond; John P. Martin. 1989. “Potential Output in the Seven Major OECD Countries”, OECD Department of Economics and Statistics Working Paper No. 66 Young, Alwyn. 1995. “The Tyranny of Numbers : Confronting the Statistical Realities of the East Asian Growth Experience”, The Quarterly Journal of Economics, Vol. 110, No. 3